SEMG for Identifying Hand Gestures using ICA

Ganesh R. Naik, Dinesh K. Kumar, Vijay Pal Singh, M. Palaniswami

2006

Abstract

There is an urgent need for establishing a simple yet robust system that can be used to identify hand actions and gestures for machine and computer control. Researchers have reported the use of multi-channel electromyogram (EMG) to determine the hand actions and gestures. The limitation of the earlier works is that the systems are suitable for gross actions, and when there is one prime-mover muscle involved. This paper reports overcoming the difficulty by using independent component analysis to separate muscle activity from different muscles and classified using backpropogation neural networks. The system is tested and found to be effective in classifying EMG.

References

1. Schlenzig, J., Hunter, E., Jain, R. : Vision based hand gesture interpretation using recursive estimation, Vol. 2. Twenty-Eighth Asilomar Conference on Signals, Systems and Computers, (1994) 1267 - 1271
2. Rehg, J. M., Kanade, T. : Vision-based hand tracking for human-computer interaction, IEEE Workshop on Motion of Non-Rigid and Articulated Objects, (1994) 16 - 22
3. Pavlovic, V. I., Sharma, R., Huang, T. S. : Visual interpretation of hand gestures for humancomputer interaction, Vol. 19. IEEE Transactions on Pattern Analysis and Machine Intelligence, (1997) 677 - 695
4. Cheron, G., Draye, J., Bourgeios, M., Libert, G. : A Dynamic Neural Network Identification of Electromyography and Arm Trajectory Relationship During Complex Movements, Vol. 43. IEEE Trans. Biomed. Eng, (1996) 552 - 558
5. Koike, Y., Kawato, M. : Human Interface Using Surface Electromyography Signals, Vol. 79. Electronics and Communications, Japan, (1996) 15 - 22
6. Djuwari, D., Kumar, D. K., Polus, B., Raghupathy, S. : Multi-step independent component analysis for removing cardiac artefacts from back semg signals, 8th Australian and New Zealand Intelligent Information Systems Conference, Australia, (2003)
7. Greco, A., Costantino, D., Morabito, F. C., Versaci, M. A. : A Morlet wavelet classification technique for ICA filtered SEMG experimental data, Vol. 1. Neural Networks Proceedings of the International Joint Conference, (2003) 66 - 71
8. Hideo, Nakamura., Masaki, Yoshida., Manabu, Kotani., Kenzo, Akazawa., Toshio, Moritani. : The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains, Vol. 14. Journal of Electromyography and Kinesiology, (2004) 423 - 432
9. Yong, Hu., Li, X. H., Xie, X. B., Pang, L. Y., Yuzhen, Cao., Luk, K. D. K. : Applying Independent Component Analysis on ECG Cancellation Technique for the Surface Recording of Trunk Electromyography, IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai (2005)
10. Hyvarinen, A.,Karhunen, J., Oja, E. :Independent Component Analysis, John Wiley, New York (2001)

Paper Citation

in Harvard Style

R. Naik G., K. Kumar D., Pal Singh V. and Palaniswami M. (2006). SEMG for Identifying Hand Gestures using ICA . In Proceedings of the 2nd International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2006) ISBN 978-972-8865-67-2, pages 61-67. DOI: 10.5220/0001223500610067

in Bibtex Style

@conference{bpc06,
author={Ganesh R. Naik and Dinesh K. Kumar and Vijay Pal Singh and M. Palaniswami},
title={SEMG for Identifying Hand Gestures using ICA},
booktitle={Proceedings of the 2nd International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2006)},
year={2006},
pages={61-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001223500610067},
isbn={978-972-8865-67-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2006)
TI - SEMG for Identifying Hand Gestures using ICA
SN - 978-972-8865-67-2
AU - R. Naik G.
AU - K. Kumar D.
AU - Pal Singh V.
AU - Palaniswami M.
PY - 2006
SP - 61
EP - 67
DO - 10.5220/0001223500610067